Overall, the tumors in the fusion-positive group tended to be solid, with a central location, and a higher value for SUVmax than those in the fusion-negative group. In addition, in the fusion-positive group, the values for kurtosis and inverse variance on 2- and 3-voxel distances were lower, whereas the mass, CT numbers or HU at the 97.5th percentile on histogram, homogeneity on 1-voxel distance, contrast and cluster shade on 1-, 2-, and 3-voxel distances were higher than in the fusion-negative group (see Table S1, http://links.lww.com/MD/A460).
Clinicoradiologic Comparison Task Between ALK vs ROS1/RET Fusion-Positive Tumors
A comparison of the clinicoradiological characteristics between the patients with ALK and ROS1/RET fusions is provided in Table 5. Tumor stage, central location, SUVmax, homogeneity on 1-, 2-, and 3-voxel distances, and sum mean on 2-voxel distance were significantly different between the 2 groups (P = 0.042, 0.017, 0.005, 0.030, 0.023, 0.028, and 0.049, respectively).
See Appendix S5, http://links.lww.com/MD/A460
Profiling various predictive biomarkers for cancer cells may further improve clinical outcomes and reduce the toxicity levels of antineoplastic drugs.25 However, most histologic approaches only involve small biopsies or cytological specimens and are therefore limiting due to the heterogeneity and invasiveness of the tumor. Furthermore, fusion molecular testing is not currently cost effective.6 The linkage of genetic information and clinical and imaging data is crucial to understanding the interplay between all of the relevant parameters and necessary to establish effective patient stratification and reliable treatment strategies in limited tissue settings. In this study, we identified clinical and imaging predictors for ALK/ROS1/RET fusion-positive lung adenocarcinoma and found that a combination of imaging parameters and clinical features has the potential to improve the differentiation of fusion-positive tumors from fusion-negative lung adenocarcinomas.
It is now known that ALK and ROS1/RET fusion-positive lung adenocarcinomas represent up to 5% and 1% to 2% of all primary NSCLCs, respectively. Due to the relatively recent discovery and low prevalence of fusion-positive lung adenocarcinomas,8,11,12,26,27 little is known regarding the tumors’ imaging characteristics and their relationship to the fusion-positive molecular phenotype. A few studies regarding imaging-based identification of ALK fusion-positive tumors using CT or PET in lung adenocarcinoma have been reported to date18,19,28,29; however, these studies were relatively subjective studies in that they only included qualitative CT variables. Moreover, imaging-based identification of ROS1/RET fusion-positive tumors in NSCLC has yet to receive much attention.
Radiomics is an emerging field that converts imaging data into a high-dimensional mineable feature space using a great number of automatically extracted data-characterization algorithms.30,31 The present study found significant radiomics-based predictors for fusion-positive tumors. These parameters are mainly quantitative; add to prior established clinical and morphologic characteristics such as gender, age, history of smoking and solidity on CT scan.11,26,29,32,33 Solidity and central tumor location, which were validated in a prior study, were also selected as fusion-positive predictors.19,29 Our results suggest the possible value of a combination of clinical and imaging parameters for genetic status prediction beyond visual assessment. A key goal of imaging is “personalized medicine,” where treatment is increasingly tailored to the specific characteristics of each patient, and may be based on molecular characterization using genomic technologies.34 In addition, the increasing desire for personalized and optimized therapy requires an advanced diagnostic tool, such as radiomics as used in our study, to predict treatment response more accurately.
Several investigations had shown that ROS1/RET fusion-positive lung adenocarcinoma has clinicopathologic similarities to ALK fusion-positive lung cancers, including young age at onset, nonsmoking history, and pathological exhibition of a “mucinous cribriform pattern” and a “solid signet-ring cell pattern.”8,10–15 In addition, several recent studies have found structural similarities at the molecular level between ALK and ROS1, and ALK and RET, particular in the kinase domains.35–37 Consequently, lung adenocarcinoma patients harboring the ROS1 or RET fusions benefit from crizotinib, similar to patients harboring ALK fusion.3,7 Thus, the basic molecular structural similarity may influence clinicopathologic similarity and subsequent similarity in imaging. With these clinicopathologic and molecular similarities between ALK, ROS1, and RET fusion-positive lung cancers, we additionally assessed whether there are common clinical and imaging features between the ALK and ROS1/RET fusion-positive groups. We found that ALK, ROS1, and RET fusion-positive lung cancers shared most clinicoradiologic features. However, compared to the ALK fusion-positive group, the ROS1/RET fusion-positive group had a lower SUVmax, whereas the ALK fusion-positive group had a higher SUVmax. This result is remarkable considering the pathologic similarity between the 2 groups. Also, tumor stage, central location, homogeneity on 1-, 2-, and 3-voxel distances, and sum mean on 2-voxel distance were significantly different between the 2 groups. Further verification of this finding in larger cohorts is warranted.
Measuring textural heterogeneity on CT or PET has the advantage of being relatively easy to perform, and the degree of the textural heterogeneity has been shown to correlate with patient outcome in esophageal and colorectal cancers, as well as NSCLC.38–41 These methods assess how grainy or coarse a tumor appears on imaging. Furthermore, the use of relative texture analysis allows the effect of variations in acquisition parameters (between the feasibility and validation data-sets) on lung tumor texture to be minimized, therefore making this approach applicable across centers.
Despite the advantages of utilizing a large cohort for validation, this analysis has several limitations. First, the data are retrospective and limited to Eastern Asian populations; thus, the findings may not be applicable to other populations. Second, owing to the relatively small number of ROS1/RET fusion-positive cases, our results are limited in their ability to achieve generalized statistical power. However, we included a comparatively large number of cases, given very low frequency of ROS1/RET fusions. In any case, further studies with a larger sample of ROS1/RET fusion-positive cases are needed to ultimately unravel the clinical and imaging relevance of ROS1/RET rearrangement. We believe our result is meaningful in terms of building baseline research data for the next relevant study. Third, we could not perform external validation using an independent population. However, we believe our findings and the comprehensive CT imaging approach described herein are meaningful, because we conducted this study with a large number of patients and we attempted to perform tenfold cross-validation as a method of internal validation.
In conclusion, ALK/ROS1/RET fusion-positive lung adenocarcinomas possess certain clinical and imaging features, enabling good discrimination of fusion-positive from fusion-negative lung adenocarcinomas. ROS1/RET fusion-positive tumors share most clinicoradiologic features with ALK fusion-positive tumors. The combination of imaging parameters with clinical features may provide added diagnostic benefit in identifying fusion-positive lung adenocarcinomas by CT imaging. This approach can have a large impact as imaging is routinely used in clinical practice in all stages of diagnoses and treatment. The results of this study may help develop treatment strategies and define categories of gene tests for ALK, ROS1, and RET fusion-positive lung cancer.
A guarantor of this study is HYL who takes responsibility for the content of the manuscript, including the data and analysis. HYL, HJY, IS, and JHC had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. J-HK, Y-LC, HK, GL, KSL, and JK contributed substantially to the study design, data analysis, and interpretation, and the writing of the manuscript.
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